Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform
Purpose To evaluate the effectiveness of C-LAB ® , an artificial intelligence (AI) platform, in extracting, structuring, and centralizing biomarker data from breast cancer pathology reports within the challenging, heterogeneous dataset of the Institut de Cancérologie de l’Ouest (ICO). Methods C-LAB...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-02-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251323110 |
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| author | Florent Le Borgne Camille Garnier Camille Morisseau Yanis Navarrete Yanina Echeverria Juan Mir Jaume Calafell Tanguy Perennec Olivier Kerdraon Jean-Sébastien Frenel Judith Raimbourg Mario Campone Maria Fe Paz François Bocquet |
| author_facet | Florent Le Borgne Camille Garnier Camille Morisseau Yanis Navarrete Yanina Echeverria Juan Mir Jaume Calafell Tanguy Perennec Olivier Kerdraon Jean-Sébastien Frenel Judith Raimbourg Mario Campone Maria Fe Paz François Bocquet |
| author_sort | Florent Le Borgne |
| collection | DOAJ |
| description | Purpose To evaluate the effectiveness of C-LAB ® , an artificial intelligence (AI) platform, in extracting, structuring, and centralizing biomarker data from breast cancer pathology reports within the challenging, heterogeneous dataset of the Institut de Cancérologie de l’Ouest (ICO). Methods C-LAB ® was deployed at the ICO to analyze HER2 and hormonal receptor data from breast cancer pathology reports. During the development phase, 292 anatomic pathology reports were used to design and refine the rule-based extraction algorithm through an iterative process of monitoring and adjustments. After finalizing the algorithm, it was applied to a total of 2323 anatomic pathology reports. To evaluate the platform's accuracy, performance metrics could only be calculated for a subset of these reports that were also available in the structured National Epidemiological Strategy and Medical Economics (ESME) database. Out of the 2323 pathology reports belonging to 487 patients analyzed by C-LAB ® , 666 corresponded to 97 patients present in the ESME database. These reports were used as the gold standard for performance assessment, as ESME provides structured data against which the outputs of the C-LAB ® algorithm could be compared. Results C-LAB ® achieved over 80% agreement with human extractions (precision, recall, and F1-score) in structuring biomarker data from complex, unstructured pathology reports, despite dataset variability and optical character recognition errors. While the ESME database served as a benchmark, its reliance on single manual data entry without secondary review introduces potential inaccuracies, suggesting the observed performance reflects close alignment between human and algorithmic extractions rather than absolute accuracy. C-LAB ® demonstrates significant potential to reduce manual workload, centralize data, and enable scalable, real-time reporting. Conclusion AI technologies like C-LAB ® show significant potential in creating accessible and actionable digital factories from complex pathology data, aiding in the precision management of diseases such as breast cancer diagnostics and treatment. |
| format | Article |
| id | doaj-art-0886b4a244ff41cebc73f07c2e60d2cc |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-0886b4a244ff41cebc73f07c2e60d2cc2025-08-20T03:11:15ZengSAGE PublishingDigital Health2055-20762025-02-011110.1177/20552076251323110Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platformFlorent Le Borgne0Camille Garnier1Camille Morisseau2Yanis Navarrete3Yanina Echeverria4Juan Mir5Jaume Calafell6Tanguy Perennec7Olivier Kerdraon8Jean-Sébastien Frenel9Judith Raimbourg10Mario Campone11Maria Fe Paz12François Bocquet13 Data Factory & Analytics Department, , Nantes-Angers, France Connect By Circular Lab, Madrid, Spain Data Factory & Analytics Department, , Nantes-Angers, France Connect By Circular Lab, Madrid, Spain Connect By Circular Lab, Madrid, Spain Connect By Circular Lab, Madrid, Spain Connect By Circular Lab, Madrid, Spain Department of Radiation Oncology, , Nantes-Angers, France Department of Pathology, , Nantes-Angers, France Center for Research in Cancerology and Immunology Nantes-Angers, Nantes University and Angers University, Nantes-Angers, France Center for Research in Cancerology and Immunology Nantes-Angers, Nantes University and Angers University, Nantes-Angers, France Center for Research in Cancerology and Immunology Nantes-Angers, Nantes University and Angers University, Nantes-Angers, France Connect By Circular Lab, Madrid, Spain Law and Social Change Laboratory, Faculty of Law and Political Sciences, Nantes University, Nantes, FrancePurpose To evaluate the effectiveness of C-LAB ® , an artificial intelligence (AI) platform, in extracting, structuring, and centralizing biomarker data from breast cancer pathology reports within the challenging, heterogeneous dataset of the Institut de Cancérologie de l’Ouest (ICO). Methods C-LAB ® was deployed at the ICO to analyze HER2 and hormonal receptor data from breast cancer pathology reports. During the development phase, 292 anatomic pathology reports were used to design and refine the rule-based extraction algorithm through an iterative process of monitoring and adjustments. After finalizing the algorithm, it was applied to a total of 2323 anatomic pathology reports. To evaluate the platform's accuracy, performance metrics could only be calculated for a subset of these reports that were also available in the structured National Epidemiological Strategy and Medical Economics (ESME) database. Out of the 2323 pathology reports belonging to 487 patients analyzed by C-LAB ® , 666 corresponded to 97 patients present in the ESME database. These reports were used as the gold standard for performance assessment, as ESME provides structured data against which the outputs of the C-LAB ® algorithm could be compared. Results C-LAB ® achieved over 80% agreement with human extractions (precision, recall, and F1-score) in structuring biomarker data from complex, unstructured pathology reports, despite dataset variability and optical character recognition errors. While the ESME database served as a benchmark, its reliance on single manual data entry without secondary review introduces potential inaccuracies, suggesting the observed performance reflects close alignment between human and algorithmic extractions rather than absolute accuracy. C-LAB ® demonstrates significant potential to reduce manual workload, centralize data, and enable scalable, real-time reporting. Conclusion AI technologies like C-LAB ® show significant potential in creating accessible and actionable digital factories from complex pathology data, aiding in the precision management of diseases such as breast cancer diagnostics and treatment.https://doi.org/10.1177/20552076251323110 |
| spellingShingle | Florent Le Borgne Camille Garnier Camille Morisseau Yanis Navarrete Yanina Echeverria Juan Mir Jaume Calafell Tanguy Perennec Olivier Kerdraon Jean-Sébastien Frenel Judith Raimbourg Mario Campone Maria Fe Paz François Bocquet Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform Digital Health |
| title | Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform |
| title_full | Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform |
| title_fullStr | Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform |
| title_full_unstemmed | Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform |
| title_short | Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform |
| title_sort | structuring and centralizing breast cancer real world biomarker data from pathology reports through c lab artificial intelligence platform |
| url | https://doi.org/10.1177/20552076251323110 |
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